Efficacy and Safety of Rovalpituzumab Tesirine Compared With Topotecan as Second-Line Therapy in DLL3-High SCLC: Results From the Phase 3 TAHOE Study
Bibliographic record
Abstract
Introduction DLL3, an atypical Notch ligand, is expressed in SCLC tumors but is not detectable in normal adult tissues. Rovalpituzumab tesirine (Rova-T) is an antibody-drug conjugate containing a DLL3-targeting antibody tethered to a cytotoxic agent pyrrolobenzodiazepine by means of a protease-cleavable linker. The efficacy and safety of Rova-T compared with topotecan as second-line therapy in patients with SCLC expressing high levels of DLL3 (DLL3-high) was evaluated. Methods The TAHOE study was an open-label, two-to-one randomized, phase 3 study comparing Rova-T with topotecan as second-line therapy in DLL3-high advanced or metastatic SCLC. Rova-T (0.3 mg/kg) was administered intravenously on day 1 of a 42-day cycle for two cycles, with two additional cycles available to patients who met protocol-defined criteria for continued dosing. Topotecan (1.5 mg/m 2 ) was administered intravenously on days 1 to 5 of a 21-day cycle. The primary end point was overall survival (OS). Results Patients randomized to Rova-T (n = 296) and topotecan (n = 148) were included in the efficacy analyses. The median age was 64 years, and 77% had the extensive disease at initial diagnosis. The median OS (95% confidence interval) was 6.3 months (5.6–7.3) in the Rova-T arm and 8.6 months (7.7–10.1) in the topotecan arm (hazard ratio, 1.46 [95% confidence interval: 1.17–1.82]). An independent data monitoring committee recommended that enrollment be discontinued because of the shorter OS observed with Rova-T compared with topotecan. Safety profiles for both drugs were consistent with previous reports. Conclusions Compared with topotecan, which is the current standard second-line chemotherapy, Rova-T exhibited an inferior OS and higher rates of serosal effusions, photosensitivity reaction, and peripheral edema in patients with SCLC. A considerable unmet therapeutic need remains in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".